Method and system for preventing upload of multimedia content with objectionable content into a server

ABSTRACT

The present invention relates to the field of content identification and more particularly to detection and identification of objectionable content present in a multimedia content. The objectionable content is detected before the upload of the content to a server or social media and it alerts the user about the presence of objectionable content based on the intelligent analytics. Further, during the alerting process the proposed mechanism is configured to consider the consequences of publishing or uploading the given content to a server or social media. Further, it also analyses the potential viewers, their profile, profile of the characters in the multimedia content.

TECHNICAL FIELD

The present disclosure relates to the field of processing of multimediacontent. Particularly, but not exclusively, the present disclosurerelates to a method and system for detection and identification ofobjectionable content in a multimedia content.

BACKGROUND

Nowadays social media is becoming very popular. Apart from connectingpeople it has also become a key platform for advertisement, idea orthought sharing, campaigning, and job recruitment. Users of social mediapost various kinds of text, images, speeches, news items, videos orcombinations thereof. Along with various benefits, it comes with risksassociated to misleading large number of people, promotion of fake news,sharing of objectionable and inappropriate content such as morphedimages, hate speeches, glorifying of terrorism, violence enticement, andthe like.

To solve a problem of objectionable content, social media platforms haveimplemented automated filtering and censoring mechanisms. The mechanismsinclude analysis of content by implementing cognitive mechanisms thatidentify the various objects being depicted in the image/video andaccordingly, censors the content and notifies the user post upload.

Additionally, some of the social media platforms have implemented amanual content censorship process where users perform a manual review ofcontent flagged by an automated system to minimize the objectionablecontent on the platform. Moreover, in some implementations, we see botsor crawlers reviewing popular and rapidly proliferating content andperforming checks against system defined parameters based on the userupload history, affiliations of the user, content validation againstother known trusted sources, and the like, and thereby taking a decisionon the content censorship.

One existing method assigns a rating to text content based on keywordspresent in text and after the text content is uploaded. Another existingmethod assigns a probability of finding the objectionable content in aregion of image or video or content under analysis. It is achieved basedon feedback from various users in the social media to ascertain categoryof the content uploaded. Further, one can make use of a global dataaudit for blocking inappropriate content after it has been uploaded.

An issue with the existing methods is that the methods are mostlyrestricted to image or video cognition and object identificationtechniques. Users can dupe such filters or censoring mechanisms by usingvarious content editing techniques such as applying masks, filters, andthe like, that prevent automated characterization and identification ofobjectionable content within such images or videos.

Conventional approaches are grossly insufficient to perform aqualitative analysis of such content that border a central objectionabletheme. This is especially true for content that promote a camouflagedobjectionable agenda such as terrorism or far right or white supremacythemes through misleading and instigating vicious speeches or content.Such content is difficult to censor through existing prior artmechanisms since individual objects being depicted in the content maynot necessarily be offensive and therefore are not filterable bycensorship mechanisms. For example, a write-up about a prominentcelebrity but promoting hatred and violence against the celebritythrough misquoted facts, is missed out from being marked asobjectionable content due to lack of content analysis.

Further, an issue with the existing prior arts is that they block orremove the objectionable content from the social media after it isuploaded and proliferated to an extent. There is no mechanism to warnthe user before upload, while also indicating the objectionable part.

The information disclosed in this background of the disclosure sectionis only for enhancement of understanding of the general background ofthe invention and should not be taken as an acknowledgement or any formof suggestion that this information forms the prior art already known toa person skilled in the art.

SUMMARY

The shortcomings of the prior art are overcome, and additionaladvantages are provided through the provision of method of the presentdisclosure.

Additional features and advantages are realized through the techniquesof the present disclosure. Other embodiments and aspects of thedisclosure are described in detail herein and are considered a part ofthe claimed disclosure.

An embodiment of the present disclosure discloses a method of preventingupload of multimedia content with objectionable content into a server.The method comprises receiving the multimedia content from a user. Next,the objectionable content present in the multimedia content isidentified. Further, an alert is provided to the user about theobjectionable content present in the multimedia content while uploadingto the server. Further, the objectionable content present in themultimedia content is deleted upon receiving instructions from the userin response to the alert provided to the user, thereby preventing theupload of the multimedia content with the objectionable content to theserver.

An embodiment of the present disclosure discloses a computing unit forpreventing upload of multimedia content with objectionable content intoa server. The computing unit includes a processor and a memorycommunicatively coupled to the processor, wherein the memory stores theprocessor instructions, which, on execution, causes the processor toreceive the multimedia content from a user; to identify theobjectionable content present in the multimedia content; to provide analert to the user about the objectionable content present in themultimedia content while uploading to the server; and to delete theobjectionable content present in the multimedia content upon receivinginstructions from the user in response to the alert provided to theuser, thereby preventing the upload of the multimedia content with theobjectionable content to the server.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS

The novel features and characteristic of the disclosure are set forth inthe appended claims. The disclosure itself, however, as well as apreferred mode of use, further objectives and advantages thereof, willbest be understood by reference to the following detailed description ofan illustrative embodiment when read in conjunction with theaccompanying figures. One or more embodiments are now described, by wayof example only, with reference to the accompanying figures wherein likereference numerals represent like elements and in which:

FIG. 1 shows an architecture diagram of a computing unit to identify theobjectionable content present in a multimedia content before upload, inaccordance with an embodiment of the present disclosure;

FIG. 2 shows an internal architecture of a computing unit foridentifying the objectionable content present in a multimedia contentbefore upload, in accordance with embodiments of the present disclosure;

FIG. 3 shows an exemplary flow chart illustrating method steps forpreventing the upload of the multimedia content with the objectionablecontent before upload, in accordance with embodiments of the presentdisclosure;

FIG. 4 shows an exemplary flow chart illustrating method steps foridentifying the objectionable content present in the multimedia content,in accordance with embodiments of the present disclosure;

FIG. 5 shows an exemplary context graph, in accordance with anembodiment of the present disclosure, in accordance with embodiments ofthe present disclosure;

FIG. 6 shows an exemplary table for computing the sensitivity index, inaccordance with an embodiment of the present disclosure; and

FIG. 7 shows a general-purpose computer system to identify theobjectionable content present in a multimedia content before upload inaccordance with embodiments of the present disclosure.

It should be appreciated by those skilled in the art that any blockdiagrams herein represent conceptual views of illustrative systemsembodying the principles of the present subject matter. Similarly, itwill be appreciated that any flow charts, flow diagrams, statetransition diagrams, pseudo code, and the like represent variousprocesses which may be substantially represented in computer readablemedium and executed by a computer or processor, whether or not suchcomputer or processor is explicitly shown.

DETAILED DESCRIPTION

In the present document, the word “exemplary” is used herein to mean“serving as an example, instance, or illustration.” Any embodiment orimplementation of the present subject matter described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other embodiments.

While the disclosure is susceptible to various modifications andalternative forms, specific embodiment thereof has been shown by way ofexample in the drawings and will be described in detail below. It shouldbe understood, however that it is not intended to limit the disclosureto the particular forms disclosed, but on the contrary, the disclosureis to cover all modifications, equivalents, and alternative fallingwithin the scope of the disclosure.

The terms “includes”, “comprising”, or any other variations thereof, areintended to cover a non-exclusive inclusion, such that a setup, deviceor method that includes a list of components or steps does not includeonly those components or steps but may include other components or stepsnot expressly listed or inherent to such setup or device or method. Inother words, one or more elements in a system or apparatus proceeded by“includes . . . a” does not, without more constraints, preclude theexistence of other elements or additional elements in the system orapparatus.

The figures and the following description relate to various embodimentsby way of illustration only. It should be noted that from the followingdiscussion, alternative embodiments of the structures and methodsdisclosed herein will be readily recognized as viable alternatives thatmay be employed without departing from the principles discussed herein.Reference will now be made in detail to several embodiments, examples ofwhich are illustrated in the accompanying figures. It is noted thatwherever practicable similar or like reference numbers may be used inthe figures and may indicate similar or like functionality.

Embodiments of the present disclosure relates to a method and system forpreventing uploading of multimedia content with objectionable orinappropriate content into a server. The method comprises of identifyingone or more characters or one or more objects along with theinteractions between them and the environment or the context or thetheme of the multimedia content. Further, sentiment is associated withone or more characters, the interactions and the theme identified. Next,the objectionable content is identified, and potential proliferation isanalyzed. Finally, the user is alerted about the objectionable contentpresent in the multimedia content.

FIG. 1 illustrates an exemplary environment to identify theobjectionable content present in a multimedia content before upload, inaccordance with an embodiment of the present disclosure.

In some implementation, the environment includes a computing unit 100, auser device 101 and a database 102. In an embodiment, the user device101 and the database 102 may be connected to the computing unit 100through a network 103. The computing unit 100 receives a multimediacontent from the user device 101 before the upload. The computing unit100 identifies the objectionable content present in the multimediacontent and its potential proliferation before the upload using thedatabase 102. The computing unit 100 further alerts the user regardingthe objectionable content and gives an option to the user tomodify/remove the objectionable content present in the multimediacontent. Upon removal or modification, the multimedia content isuploaded to the server (not shown in the figure). In an embodiment, thecomputing unit 100 may include for example, mobile phone, laptop,server. A person skilled in the art would understand that any othercomputing unit 100 which may be used to communicate with the user device101, not mentioned explicitly, may also be used in the presentdisclosure. In an embodiment, the multimedia content may include text,image, video and combinations thereof.

In an embodiment the computing unit 100 may be present in the userdevice 101 or the server may act as a computing unit 100.

FIG. 2 shows a detailed block diagram of a computing unit 100 foridentifying the objectionable content present in a multimedia contentbefore upload, in accordance with embodiments of the present disclosure.The computing unit 100 may include at least one Central Processing Unit(CPU or processor) 203 and a memory 202 storing instructions executableby the at least one processor 203. The processor 203 may include atleast one data processor for executing program components for executinguser or system-generated requests. The memory 202 is communicativelycoupled to the processor 203. The computing unit 100 further comprisesan Input/Output (I/O) interface 201. The I/O interface 201 is coupledwith the processor 203 through which an input signal or/and an outputsignal is communicated.

In some implementations, the computing unit 100 may include data 204 andmodules 210. As an example, the data 204 is stored in the memory 202configured in the computing unit 100 as shown in the FIG. 2. In oneembodiment, the data 204 may include, for example, topic data 205, themedata 206, interaction data 207, meta data 208 and other data 209. In theillustrated FIG. 2, modules 210 are described herein in detail.

In some embodiments, data 204 may be stored in the memory 202 in form ofvarious data structures. Additionally, the data 204 may be organizedusing data models, such as relational or hierarchical data models. Theother data 209 may store data, including temporary data and temporaryfiles, generated by the modules 210 for performing the various functionsof computing unit 100.

In an embodiment, the topic data 205 may include nouns and verbs, forexample names of persons and animals. As an example, consider a text“John is hunting a bird sitting on a branch of the tree”. With respectto the example, the person named “John”, “bird” and “tree” can be storedas the topic data 205.

In an embodiment, the theme data 206 may include names of places and theenvironment of the multimedia content. With respect to theabove-mentioned example, the action hunting usually happens in a forestor a jungle therefore the “forest” can be stored as the theme data 206.

In an embodiment, the interaction data 207 may include the actionsperformed by the characters or the objects in the multimedia content.With respect to the aforementioned example, the action “hunting” can bestored as the interaction data 207.

In an embodiment, the metadata 208 may include captions of the image,tags associated with the images or the video, the comments written to amultimedia content in a social media.

In some embodiments, the data 204 stored in the memory 202 may beprocessed by the modules 210 of the computing unit 100. The modules 210may be stored within the memory 202. In an example, the modules 210communicatively coupled to the processor 203 configured in the computingunit 100, may also be present outside the memory 202 as shown in FIG. 2and implemented as hardware. As used herein, the term modules 210 mayrefer to an application specific integrated circuit (ASIC), anelectronic circuit, a processor (shared, dedicated, or group) and memorythat execute one or more software or firmware programs, a combinationallogic circuit, and/or other suitable components that provide thedescribed functionality.

In one implementation, the modules 210 may include, for example, a topicidentification module 211, a theme identification module 212, acharacter interaction identification module 213, a sentiment associationmodule 214, a content immersion module 215, a user analysis module 216and an alert generation module 217. It will be appreciated that themodules 210 may be represented as a single module or a combination ofdifferent modules.

The processor 203 receives the multimedia content from the user device101. In an embodiment, the topic identification module 211, the themeidentification module 212 and the character interaction identificationmodule 213 may receive the multimedia content from the processor 203.Further, the topic identification module 211 identifies the subjects,the objects and the characters present in the multimedia content. Thetheme identification module 212 identifies the predicates, constructs acontext graph and based on the context graph the overall theme or theenvironment of the multimedia content is identified. The characterinteraction identification module 213 recognizes the actions performedbetween one or more subjects, the one or more objects and the one ormore characters.

In an embodiment, the partial results of the topic and the themeidentification modules are stored in the database 102. The sentimentassociation module 214, using the partial results of the other modulesassociates sentiments to the objects, the characters, and the subjectsand the actions performed by them using one or more machine learningalgorithms, for example Long Short Term Memory (LSTM). A person skilledin the art would understand that any other machine learning algorithmnot mentioned explicitly, may also be used in the present disclosure.

In an embodiment, the content immersion module 215 using the associatedsentiments identifies the objectionable content present in themultimedia content. The user analysis module 216 computes thesensitivity index and predicts the potential proliferation for theidentified objectionable content. Further, the alert generation module217 generates an alert to the user indicating the objectionable contentin the multimedia content and its potential proliferation.

FIG. 3 shows an exemplary flow chart illustrating method steps forpreventing the upload of the multimedia content with the objectionablecontent, in accordance with an embodiment of the present disclosure.

The order in which the method 300 is described is not intended to beconstrued as a limitation, and any number of the described method blockscan be combined in any order to implement the method. Additionally,individual blocks may be deleted from the methods without departing fromthe spirit and scope of the subject matter described herein.Furthermore, the method can be implemented in any suitable hardware,software, firmware, or combination thereof.

As illustrated in FIG. 3, the method 300 may include one or more stepsfor preventing the upload of the multimedia content with theobjectionable content. At the step 301, the computing unit 100 receivesthe multimedia content from the user through a network 103 as shown inFIG. 1. At, the step 302, the computing unit 100 process the receivedmultimedia content to identify the objectionable content before upload.Further, the FIG. 4 illustrates an exemplary method to identify theobjectionable content present in the multimedia content before theupload, in accordance with an embodiment of the present disclosure.

At the step 401 the topic and the theme are derived. The step comprisesof identifying all the topics or characters or subjects or objectspresent in the multimedia content. Next, the predicates and the usercontext are identified, along with the topics identified earlier acontext graph is constructed. From the context graph the commonality ofthe specific context is identified as the theme or the environment ofthe multimedia content.

In an embodiment, for text-based multimedia content, to derive thetopic, an attention word from the text is identified. To get theattention word, subject and predicate in the describing sentences areidentified and the subject is separated out. A specific example, in thetext “A bird is sitting on a tree” the topic is “bird”. In oneembodiment, if a sentence includes more than one subject, then based onthe relative frequency of each subject, the subject with the highestrelative frequency is taken as the attention topic. A person skilled inthe art would understand that any other technique to determine the topicfrom the text, not mentioned explicitly, may also be used in the presentdisclosure.

In an embodiment, for image-based multimedia content, the objects in theimages are identified based on image object recognition algorithms.Based on the relative visual prominence, scene context and imagecaption/title/metadata, the topic is determined. As an example, for animage of a bird sitting on a tree, the topic bird is identified througheither object recognition of the bird or through the title of the imageor through user tagging of similar images as “bird”. A person skilled inthe art would understand that any other technique which may be used todetermine object from the image, not mentioned explicitly, may also beused in the present disclosure.

In an embodiment, for video-based content, the accompanying audio istranscribed to text using speech analytics and then a similar process asthe text-based content is followed for topic determination. Also, as anadditional step, frame by frame image analysis of the video isperformed, to identify the objects present in the image frames as donein the image-based content described above. A person skilled in the artwould understand that any other video analysis technique, not mentionedexplicitly, may also be used in the present disclosure.

Further, in an embodiment after the identification of the topics in themultimedia content a context graph is constructed in a hierarchicalmanner that speak about the topics in a specific context. Based on thecontext graph the commonality of a specific context derived from thetopics is chosen as the theme. Further, the chosen theme is alsovalidated with the root word of the theme in the context graph.

A specific example of the context graph is shown in FIG. 5, inaccordance with an embodiment of the present disclosure. Let the usercontent comprise a text sentence as follows “A bird is sitting on atree”. Using the subject and predicate relationship the “bird” and the“tree” is identified as the topics. Further, using the context of thetopics the theme is derived. The topic “bird” can be associated with thecontext “Zoo” and “Scenery”. The topic “tree” can be associated with thecontext “forest” and “Scenery”. The commonality of the context betweenthe topics, that is “Scenery” is derived as the theme for the sentenceprovided by the user.

At the step 402, the interactions among the characters are identified.In an embodiment, the characters in the text are extracted throughparsing and extracting the named entities. The surroundings of characterdetermined by the interaction of the characters. In text datainteraction is identified through subject and predicate relation. As anexample, consider the text “Peter has supported people with money”, herethe “Peter” and “people” are the characters and the interaction isthrough “money”.

In an embodiment, the characters in the image or video are extractedthrough any of the known technique may be used for the objectextraction. Further, the interaction is extracted through a plurality oftechniques. In one embodiment, the physical adjacency of objects isconsidered as interaction, for example consider an image where “John isstanding in front of portrait”. “John” and the “portrait” are thecharacters and the relation between them is “in front of”. To extractthe said relation using physical adjacency, the image is divided in toregions using watershed/region growing algorithm. All regions next tothe region of (or within) the characters are identified, and thebackground is omitted.

Further in an embodiment, the image is also subjected to captioning.There can be more than one caption. For example,

-   -   “man is in front of portrait”    -   “men are standing before artifact”    -   “John in front of portrait”    -   “Peter and John are in front of portrait”        Some of the captions put the names such as “John” based on the        face recognition from database 102. The object in the caption        extracted as for example man/men/john, peter, portrait is then        corroborated with the interacting regions extracted from        watershed algorithm and the interaction between the characters        is derived.

At the step 403, sentiment association is done. The sentiments can benormal or objectionable (or forbidden). The sentiments are associatedwith the characters and the actions binding them by generating asentiment graph based on the plurality of parameters. A typicalsentiment graph for a text-based multimedia content is shown below:

-   -   subject→predicate→action→attributes→sentiment        The sentiment graph generation involves the following steps:    -   i. Attribute generation: The attributes of each of the        characters are populated based on, for example historical        transactions, social media opinions, internet and by the user.        The procedure works on both image/video-based content and the        text-based content.    -   ii. Action generation: The action relating the characters is        derived from the predicate. Action can carry negative sentiments        although characters themselves are normal. As a specific example        consider a text “a bike hit a boy”. The characters “bike” and        “boy” are neutral but the action “hit” contributes for the        negative sentiment.    -   iii. Sentiment generation: The sentiment is generated based on        the characters, attributes, actions and the binding predicate.        In one embodiment, an LSTM is used to train the sentiment.

As a specific example, consider an image where “John took a selfie withPeter”. If “john” is a terrorist obtained from the historical data,transactions, library or from third party sources such as websites andcriminal databases, the associated sentiment is objectionable.

At the step 404, decision is taken about the content (or parts of it) ifit is objectionable. It relates content with characters. Further, in anembodiment, the objectionable content is identified based on thesentiment association, the context-based analysis that additionallyfactor in user analysis, theme analysis, bibliographic history or originand the like. As a specific example consider an image of “A personholding a gun and shooting”. The content per se is not objectionable.But the character analysis of person identifies the person as aterrorist, then the content is objectionable.

In an embodiment after the identification of the objectionable contentin the multimedia content, the user analysis module 216, predicts thepotential proliferation of the multimedia content based on thesensitivity index. To compute the sensitivity index, a plurality ofparameters is associated with a plurality of values and a weightcorresponding to the plurality of values using the historical data fromthe database 102. The weights of the various parameters present in themultimedia content are added and normalized to get the sensitivityindex.

In an embodiment, as a specific example to compute the sensitivityindex, a table stored in the database 102 as shown in FIG. 6 is used.One of the columns of the table consists of parameters used to computesensitivity index comprising of a topic of the multimedia content,intended social media site to post the multimedia content, contacts ofthe user trying to upload the multimedia content, viewer class for themultimedia content. Further, the columns of the table consist ofplurality of values corresponding to each of the parameters and aweightage corresponding to the plurality of values. Consider theparameter “topic of the multimedia content” as shown in an exemplarytable, the values associated with the corresponding parameter is thevarious areas under which the multimedia content can be categorizedbased on the topics, the theme, the characters, the interactions amongthe characters present in the multimedia content. Further, for each ofthe values categorized, a predefined weightage is associated based onthe historical data. Using the weightage, a sensitivity index iscalculated by adding all the weights corresponding to the values presentin the multimedia content followed by normalization.

Consider an example 1, a text message “the president is a fool” forcalculating the sensitivity index using the table. The viewer class forthe example is Professional, Unemployed, hired (to criticize or praisethe president), criminal (to create disturbances), it adds the weightageto 12. The topic of the text message is politics and the correspondingweightage is 2. The contacts of the user say adds to 9 and the socialmedia for posting the text message is Facebook and the correspondingweightage is 3. Thus, the total weightage considering all the parametersis 26, after normalization the sensitivity index would be 6.5.

Consider another example 2 “Czars were incapable” for calculating thesensitivity index using the table. The viewer class is professional andthe corresponding weightage is 1. The topic of the text message isHistory and the corresponding weightage is 2. The contacts of the usersay adds to 9, the social media for posting the text message is Quoraand the corresponding weightage is 1. Thus, the total weightageconsidering all the parameters is 13, after normalization thesensitivity index would be 3.25.

Thus, as compared with example 2, the example 1 has higher sensitivityindex indicating the text message in example 1 has objectionable contentwith higher proliferation rate hence the user is alerted to modify themessage before uploading.

At the step 303, the user is alerted about the objectionable parts ofthe multimedia content along with the sensitivity index and thepotential proliferation. The user is given an option to remove or modifythe objectionable content.

At the step 304, if the user modifies or deletes the objectionablecontent, the sensitivity values and potential proliferation isrecomputed, and the above steps are revisited. If there is noobjectionable content, the multimedia content gets uploaded.

In an embodiment, if the user is unwilling to change, the content isuploaded with a special note to social media about the details of theobjection. Such annotated content is flagged automatically and mayrequire intervention of social media site moderators and may beprevented from appearing to other social media users or viewersimmediately, till a site moderator performs a review of the annotatedcontent.

In another embodiment, the application or any computing unit 100 afteridentifying the objectionable content in the multimedia content canblock the user from uploading the multimedia content.

In another embodiment, the application or any computing unit 100 canremove the objectionable content upon identifying the objectionablecontent in the multimedia content and further upload the multimediacontent.

Computer System

FIG. 7 illustrates a block diagram of an exemplary computer system 700for implementing embodiments consistent with the present disclosure. Inan embodiment, the computer system 700 is used to implement the methodfor identifying the objectionable content in the multimedia contentbefore upload. The computer system 700 may comprise a central processingunit (“CPU” or “processor”) 702. The processor 702 may comprise at leastone data processor for executing program components for dynamic resourceallocation at run time. The processor 702 may include specializedprocessing units such as integrated system (bus) controllers, memorymanagement control units, floating point units, graphics processingunits, digital signal processing units, etc.

The processor 702 may be disposed in communication with one or moreinput/output (I/O) devices (not shown) via I/O interface 701. The I/Ointerface 701 may employ communication protocols/methods such as,without limitation, audio, analog, digital, monoaural, RCA, stereo,IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC,coaxial, component, composite, digital visual interface (DVI),high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA,IEEE 802.n/b/g/n/x, Bluetooth, cellular (e.g., code-division multipleaccess (CDMA), high-speed packet access (HSPA+), global system formobile communications (GSM), long-term evolution (LTE), WiMax, or thelike), etc.

Using the I/O interface 701, the computer system 700 may communicatewith one or more I/O devices. For example, the input device 710 may bean antenna, keyboard, mouse, joystick, (infrared) remote control,camera, card reader, fax machine, dongle, biometric reader, microphone,touch screen, touchpad, trackball, stylus, scanner, storage device,transceiver, video device/source, etc. The output device 411 may be aprinter, fax machine, video display (e.g., cathode ray tube (CRT),liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasmadisplay panel (PDP), Organic light-emitting diode display (OLED) or thelike), audio speaker, etc.

In some embodiments, the computer system 700 is connected to the serviceoperator through a communication network 709. The processor 702 may bedisposed in communication with the communication network 709 via anetwork interface 703. The network interface 703 may communicate withthe communication network 709. The network interface 703 may employconnection protocols including, without limitation, direct connect,Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission controlprotocol/Internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x,etc. The communication network 709 may include, without limitation, adirect interconnection, e-commerce network, a peer to peer (P2P)network, local area network (LAN), wide area network (WAN), wirelessnetwork (e.g., using Wireless Application Protocol), the Internet,Wi-Fi, etc. Using the network interface 703 and the communicationnetwork 709, the computer system 400 may communicate with the one ormore service operators.

In some embodiments, the processor 702 may be disposed in communicationwith a memory 705 (e.g., RAM, ROM, etc. not shown in FIG. 7) via astorage interface 704. The storage interface 704 may connect to memory705 including, without limitation, memory drives, removable disc drives,etc., employing connection protocols such as serial advanced technologyattachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394,Universal Serial Bus (USB), fibre channel, Small Computer SystemsInterface (SCSI), etc. The memory drives may further include a drum,magnetic disc drive, magneto-optical drive, optical drive, RedundantArray of Independent Discs (RAID), solid-state memory devices,solid-state drives, etc.

The memory 705 may store a collection of program or database components,including, without limitation, user interface 706, an operating system707, web server 708 etc. In some embodiments, computer system 700 maystore user/application data 706, such as the data, variables, records,etc. as described in this disclosure. Such databases may be implementedas fault-tolerant, relational, scalable, secure databases such as Oracleor Sybase.

The operating system 707 may facilitate resource management andoperation of the computer system 700. Examples of operating systemsinclude, without limitation, Apple Macintosh OS X, Unix, Unix-likesystem distributions (e.g., Berkeley Software Distribution (BSD),FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat,Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, 10etc.), Apple iOS, Google Android, Blackberry OS, or the like.

In some embodiments, the computer system 700 may implement a web browser708 stored program component. The web browser 708 may be a hypertextviewing application, such as Microsoft Internet Explorer, Google Chrome,Mozilla Firefox, Apple Safari, etc. Secure web browsing may be providedusing Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer(SSL), Transport Layer Security (TLS), etc. Web browsers 708 may utilizefacilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java,Application Programming Interfaces (APIs), etc. In some embodiments, thecomputer system 700 may implement a mail server stored programcomponent. The mail server may be an Internet mail server such asMicrosoft Exchange, or the like. The mail server may utilize facilitiessuch as ASP, ActiveX, ANSI C++/C #, Microsoft .NET, CGI scripts, Java,JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server mayutilize communication protocols such as Internet Message Access Protocol(IMAP), Messaging Application Programming Interface (MAPI), MicrosoftExchange, Post Office Protocol (POP), Simple Mail Transfer Protocol(SMTP), or the like. In some embodiments, the computer system 700 mayimplement a mail client stored program component. The mail client may bea mail viewing application, such as Apple Mail, Microsoft Entourage,Microsoft Outlook, Mozilla Thunderbird, etc.

In an embodiment, the computer system 700 may comprise remote devices712. The computer system 700 may receive the multimedia content forupload from the remote devices 712 through the Communication network709.

The terms “an embodiment”, “embodiment”, “embodiments”, “theembodiment”, “the embodiments”, “one or more embodiments”, “someembodiments”, and “one embodiment” mean “one or more (but not all)embodiments of the invention(s)” unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all of theitems are mutually exclusive, unless expressly specified otherwise. Theterms “a”, “an” and “the” mean “one or more”, unless expressly specifiedotherwise.

A description of an embodiment with several components in communicationwith each other does not imply that all such components are required. Onthe contrary a variety of optional components are described toillustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be readilyapparent that more than one device/article (whether or not theycooperate) may be used in place of a single device/article. Similarly,where more than one device or article is described herein (whether ornot they cooperate), it will be readily apparent that a singledevice/article may be used in place of the more than one device orarticle or a different number of devices/articles may be used instead ofthe shown number of devices or programs. The functionality and/or thefeatures of a device may be alternatively embodied by one or more otherdevices which are not explicitly described as having suchfunctionality/features. Thus, other embodiments of the invention neednot include the device itself.

The illustrated operations of FIG. 3 and FIG. 4 show certain eventsoccurring in a certain order. In alternative embodiments, certainoperations may be performed in a different order, modified or removed.Moreover, steps may be added to the above described logic and stillconform to the described embodiments. Further, operations describedherein may occur sequentially or certain operations may be processed inparallel. Yet further, operations may be performed by a singleprocessing unit or by distributed processing units.

The system described in the present disclosure is used to warn the userabout potential proliferation of the objectionable content beforeuploading the multimedia content over the social media. Further, thesystem prevents the glorification of the objectionable content bydeleting the objectionable content in the multimedia content. Finally,the present disclosure supports both audio or text and image or videocontent together for identification of the objectionable content.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based here on. Accordingly, the disclosure of theembodiments of the invention is intended to be illustrative, but notlimiting, of the scope of the invention, which is set forth in thefollowing claims.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

REFERRAL NUMERALS: Reference number Description 100 Computing unit 101User device 102 Database 103 Network 201 I/O interface 202 Memory 203Processor 204 Data 205 Topic data 206 Theme data 207 Interaction data208 Meta data 209 Other Data 210 Modules 211 Topic Identification Module212 Theme Identification Module 213 Character Interaction IdentificationModule 214 Sentiment Association Identification Module 215 ContentImmersion Module 216 User Analysis Module 217 Alert Generation Module

1. A method of preventing upload of multimedia content withobjectionable content into a server, the method comprising: receiving,by a computing unit, the multimedia content from a user; identifying, bythe computing unit, the objectionable content present in the multimediacontent, wherein identifying the objectionable content comprises:determining one or more characters and an environment hosting the one ormore characters from the multimedia content to identify one or moretopics and a theme of the multimedia content; identifying interactionsamong the one or more characters in the multimedia content; associatingsentiments with the one or more characters and actions performed by theone or more characters; and determining a relationship between at leastone of the theme, the one or more topics, the interactions among the oneor more characters, the sentiments of the one or more characters and thesentiments of the actions; providing by the computing unit, an alert tothe user about the objectionable content present in the multimediacontent while uploading to the server; and deleting, by the computingunit, the objectionable content present in the multimedia content uponreceiving instructions from the user in response to the alert providedto the user, thereby preventing the upload of the multimedia contentwith the objectionable content to the server.
 2. (canceled)
 3. Themethod of claim 1, wherein the one or more topics in the multimediacontent comprises at least one of one or more subjects and one or moreobjects in the multimedia content.
 4. The method of claim 1, whereinidentifying the one or more topics in the multimedia content comprisinga text comprises: determining at least one attention word from a textpresent in the multimedia content, wherein the attention word isdetermined by classifying subject from predicate in the text; andidentifying relative frequency of the subject, wherein the subject isderived as topic based on relative frequency of the subject.
 5. Themethod of claim 1, further wherein identifying the one or more topics inthe multimedia content comprising an audio visual content comprises:determining at least one of the one or more objects present in themultimedia content, wherein the at least one of the one or more objectsis determined by at least one of facial recognition, user context andattributes associated with the one or more objects from historical datain the multimedia content.
 6. The method of claim 1, wherein identifyingthe theme is based on at least one of captions, and context graphs inthe multimedia content.
 7. The method of claim 1, wherein theinteractions among the one or more characters is identified byextracting at least one of predicate associated with one or moresubjects, and physical adjacency of one or more objects.
 8. The methodof claim 1, wherein the sentiments are associated by computing asentiment graph, wherein the sentiment graph is generated based onattributes table populated from historical data, action relating to theone or more characters and a machine learning algorithm.
 9. The methodof claim 1, comprises predicting a potential proliferation of themultimedia content with the objectionable content based on sensitivityindex derived from weights associated with a plurality of parameters,wherein the plurality of parameters include viewer class, type of socialmedia, the one or more topics and attributes associated with the one ormore topics, wherein the user is alerted about the predicted potentialproliferation while uploading the multimedia content to the server. 10.The method of claim 1, wherein the user is provided an option to modifythe objectionable content present in the multimedia content beforeuploading.
 11. A computing unit for preventing upload of multimediacontent with objectionable content into a server comprising: aprocessor; and a memory communicatively coupled to the processor,wherein the memory stores the processor instructions, which, onexecution, causes the processor to: receive the multimedia content froma user; identify the objectionable content present in the multimediacontent, wherein identifying the objectionable content comprises:determining one or more characters and an environment hosting the one ormore characters from the multimedia content to identify one or moretopics and a theme of the multimedia content, identifying interactionsamong the one or more characters in the multimedia content; associatingsentiments with the one or more characters and actions performed by theone or more characters; and determining a relationship between at leastone of the theme, the one or more topics, the interactions among the oneor more characters, the sentiments of the one or more characters and thesentiments of the actions; alert the user about the objectionablecontent present in the multimedia content while uploading to the server;and delete the objectionable content present in the multimedia contentupon receiving instructions from the user in response to the alertprovided to the user, thereby preventing the upload of the multimediacontent with the objectionable content to the server.
 12. (canceled) 13.The computing unit of claim 11, wherein the one or more topics in themultimedia content comprises at least one of one or more subjects andone or more objects in the multimedia content.
 14. The computing unit ofclaim 11, wherein the processor is configured to identify the one ormore topics comprises: determining at least one of the one or moreobjects present in the multimedia content, wherein the at least one ofthe one or more objects is determined by at least one of facerecognition, user context and attributes associated with the one or moreobjects from historical data in the multimedia content.
 15. Thecomputing unit of claim 11, wherein the processor is configured toidentify the one or more topics comprises: determining at least one ofthe one or more objects present in the multimedia content, wherein theat least one of the one or more objects is determined by at least one offace recognition, user context and attributes associated with the one ormore objects from historical data in the multimedia content.
 16. Thecomputing unit of claim 11, wherein the processor is configured toidentify the theme based on at least one of captions, and context graphsin the multimedia content.
 17. The computing unit of claim 11, whereinthe processor is configured to identify the interactions among the oneor more characters by extracting at least one of predicate associatedwith the one or more subjects, and physical adjacency of one or moreobjects.
 18. The computing unit of claim 11, wherein the processor isconfigured to associate the sentiments by computing a sentiment graph,wherein the sentiment graph is generated based on attributes tablepopulated from historical data, action relating to the one or morecharacters and a machine learning algorithm.
 19. The computing unit ofclaim 11, wherein the processor is configured to predict a potentialproliferation of the multimedia content with the objectionable contentbased on sensitivity index derived from weights associated with aplurality of parameters, wherein the plurality of parameters includeviewer class, type of social media, the one or more topics andattributes associated with the one or more topics, wherein the user isalerted about the predicted potential proliferation while uploading themultimedia content to the server.
 20. The computing unit of claim 11,wherein the processor is configured to provide the user an option tomodify the objectionable content present in the multimedia contentbefore uploading.